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Project

ERC POC project: DEFOG: Data science tool for epidemic forecasting (R-10150)

Several organisations active in the health care market make decisions based on an estimation of the number of individuals infected with an infectious disease: hospitals adapt the supply of beds and staff, laboratories increase diagnostic testing capacity, public health and regulatory agencies develop control strategies, pharmacies and pharmaceutical companies optimize stockpiling whereas the latter also optimize the production rate of vaccines and advertising strategies. These organisations benefit from a timely and trust-worthy prediction of the number of infected individuals. To date, infectious disease predictions tools are inadequate because they typically include limited and domain-specific data and because the translation to impact for specific stakeholders in the healthcare market is lacking. DEFOG proposes a data science solution as we will integrate classical surveillance data, pharmacy sales data, out-of-hours general practitioners data and social contact data in a novel real-time forecasting tool that will yield better and more rapid warning signals of the number of infected cases. DEFOG will produce software and know-how under intellectual property of the principal investigator which will be exploited through licence agreements with and consultancy services for various stakeholders. DEFOG will build on recent advances in mathematical modelling for infectious diseases as part of the original ERC-TransMID project. The team offers a vast amount of expertise in mathematical and infectious disease modelling, computational processing, business development and has an extensive collaborative network with research centres, regulatory agencies and companies, of which 4 entities already expressed interest in exploring the use and/or sharing data of the proposed innovative real-time disease forecasting tool.
Date:1 Oct 2019  →  Today
Keywords:disease forecasting tool, EPIDEMIOLOGY AND PUBLIC HEALTH, Infectious disease, mathematical modelling
Disciplines:Statistics